Power transmission line operation state monitoring system based on power transmission management

Abstract

The invention belongs to the technical field of power anomaly detection, and discloses a power transmission line operation state monitoring system based on power transmission management. The system comprises a line fault related data acquisition module, a line life related data acquisition module, a fault related data preprocessing module, a fault prediction value generation module, a fault judgment decision module, a fault false alarm optimization module, an equipment aging early warning module, a self-adaptive scheduling module and a visual reminding module. And analyzing the fault feature data set to obtain a fault prediction value, processing the line life related data and the preprocessed line load data to obtain equipment replacement time data, and dynamically distributing the power transmission line load based on the equipment replacement time data. The method has the remarkable advantages of being high in fault and fluctuation distinguishing capacity, large in replacement period detection effect and high in dynamic load optimization safety degree.

Description

Technical Field

[0001] The present invention relates to the technical field of power anomaly detection, and more particularly to a power transmission line operation status monitoring system based on power transmission management. Background Art

[0002] Transmission lines are implemented by using transformers to boost the voltage of the electricity generated by generators, and then connecting them to the transmission lines through control equipment such as circuit breakers. Structurally, transmission lines are divided into overhead transmission lines and cable lines. For such a large transmission line system, traditional transmission line operation status monitoring mostly relies on manual inspections, which incurs huge manpower and time costs. At the same time, due to the importance of electricity, if there is a fault in the transmission line, it will affect the normal life of people in a large area.

[0003] The patent application publication number CN119154504A discloses a transmission line operation status monitoring system based on power transmission management. Through online monitoring of the surface of the power transmission line, it is inferred whether there is a leakage risk on the current line surface, so that the feasibility of the power transmission line is ensured through online monitoring. The line operation status monitoring is carried out under the premise of ensuring operation, ensuring the timeliness of power transmission while ensuring the power transmission efficiency, and improving the comprehensiveness of line monitoring. Real-time impact analysis is carried out on the transmission period of the power transmission line to avoid the impact of the transportation environment on the power transmission line, which leads to a decrease in the transmission efficiency of the power transmission line, resulting in deviations in the online monitoring results and the inability to accurately monitor the feasibility of the execution of the power transmission line. At the same time, based on real-time impact monitoring, the timeliness of line monitoring and control can be improved, and reasonable and timely early warning and control of environmental impacts can be carried out, reducing environmental impacts and improving line operation efficiency.

[0004] However, the above-mentioned transmission line operation status monitoring system based on power transmission management, although it achieves the purpose of improving line operation efficiency by ensuring the executability of power transmission lines, improving the comprehensiveness of line detection and improving the timeliness of line monitoring and control, during the operation of the transmission line system, due to the relatively complex environment in which the transmission lines are located, it is easy to report normal fluctuations as faults, thereby causing a waste of maintenance resources. At the same time, the aging of the transmission lines will also be highly affected by the environment in which they are located, which can easily cause the service life of the transmission lines to expire prematurely. Therefore, how to optimize the accurate distinction between faults and normal fluctuations while predicting transmission line faults, monitor the aging status of transmission lines in real time, and differentiate the use of transmission lines according to the degree of aging of transmission lines has become a major problem that needs to be urgently solved in the current power transmission industry.

[0005] In view of this, the present invention proposes a transmission line operation status monitoring system based on power transmission management to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solutions, including:

[0007] A line fault related data acquisition module is used to acquire line fault related data sets, which include line length data, line resistance data, and line load data;

[0008] Line life related data acquisition module, used to collect line life related data sets, including line current data, line temperature data and ambient humidity data;

[0009] A fault-related data preprocessing module is used to preprocess line fault-related data to obtain a fault feature data set;

[0010] Furthermore, the method of pre-processing the line fault related data includes:

[0011] Q1, using linear interpolation and minimum-maximum normalization calculation formula, clean and normalize the line fault related data;

[0012] Q2, through line length data Multiply by the line resistance data , get the conductive characteristic data ;

[0013] Q3, extract redundant features from line load data. The specific calculation formula for redundant feature extraction is:

[0014] ;

[0015] Get redundant feature data ,in, is the line load data, is the standard load data;

[0016] Q4, pack the conductive feature data and redundant feature data to obtain the fault feature data set;

[0017] A fault prediction value generation module is used to analyze the fault feature data set to obtain a fault prediction value;

[0018] Furthermore, the steps of analyzing the fault feature data set include:

[0019] Step 1: Obtain a set of historical fault feature data sets stored in the database, compare them with the current time based on the timestamp, and mark them in corresponding groups from small to large. The marking results are W1, W2, W3, ..., Wn, and the marking results are used as the sample set;

[0020] Step 2: Divide the sample set into a 70% training set, a 15% test set, and a 15% validation set, and build a fault prediction model based on the sample set;

[0021] Step 3: Based on the training set, substitute the calculation formula:

[0022] ;

[0023] Get the first fault prediction value ,in, is the activation function, is the time window size of the historical fault feature dataset, For the The time decay weight factor of the historical fault feature dataset, is a composite kernel function, For the Group historical fault feature datasets, is the fault feature dataset, is the strength coefficient, For the Group and The cross-weight factor of the historical conductive characteristic data and historical redundant characteristic data of the group, For the The historical conductive characteristic data of the group, For the Historical redundant feature data of the group;

[0024] Step 4: Based on the training set, substitute the calculation formula:

[0025] ;

[0026] Get the second fault prediction value ,in, is the Euclidean distance symbol;

[0027] Step 5: Based on the training set, substitute the calculation formula:

[0028] ;

[0029] Get the third fault prediction value ,in, is the mean of the historical fault feature dataset, is the transpose operation of the matrix, is the inverse matrix of the covariance matrix;

[0030] Step 6: Calculate the fault prediction value based on the first fault prediction value, the second fault prediction value, and the third fault prediction value. The specific calculation formula is:

[0031] ;

[0032] Get the fault prediction value ,in, is the hyperbolic tangent function, is the Sigmoid activation function;

[0033] Step 7: Output the fault prediction value to the fault judgment decision module;

[0034] The fault judgment and decision module is used to analyze the fault prediction value, judge the analysis results, and obtain a fault decision report;

[0035] Furthermore, the fault prediction value is analyzed and the method of judging the analysis result includes:

[0036] W1, based on the discrimination threshold group, discriminates the fault prediction value. The specific calculation formula group for discrimination is:

[0037] ;

[0038] Get analysis results ,in, is the rate of change of the fault prediction value, is the time rate of change, is the rate of change of line length data, and are the first discrimination threshold and the second discrimination threshold respectively, For other situations;

[0039] When the analysis result is 1, it indicates that the fault prediction value is a real fault, and the fault prediction value is output to step W2. When the analysis result is 0, it indicates that the fault prediction value is a normal fluctuation and is not processed.

[0040] W2, classifies the analysis results based on the fault threshold interval (R1, R2);

[0041] When the fault prediction value is less than or equal to R1, a normal report is generated; when the fault prediction value is greater than R2 and less than or equal to R2, a fluctuation report is generated; when the fault prediction value is greater than R2, a fault report is generated;

[0042] W3, a normal report includes a statement that the current transmission line is predicted to have a low probability of failure and that staff are requested to carry out inspections according to the scheduled inspection cycle;

[0043] The fluctuation report includes an explanation of the predicted fault possibility of the current transmission line, and asks the staff to adjust the reactive power compensation and detection parameter changes;

[0044] The fault report includes a statement indicating that the current transmission line is highly likely to fail, and requests staff to isolate the transmission lines in the designated area and dispatch a nearby repair team to conduct on-site inspections and repairs.

[0045] W4, packages the normal report, fluctuation report and fault report to obtain the fault decision report;

[0046] A fault false alarm optimization module is used to optimize the fault threshold interval based on the fault prediction value to obtain a new fault threshold interval;

[0047] Furthermore, the method of optimizing the fault threshold interval based on the fault prediction value includes:

[0048] By substituting into the calculation formula group:

[0049] ;

[0050] Get the new fault threshold interval ( , ),in, is the mean of the fault prediction values, is the standard deviation of the fault prediction value;

[0051] Return the new fault threshold interval and substitute it into step W2 in the fault judgment decision module;

[0052] The equipment aging warning module is used to process line life-related data and pre-processed line load data to obtain equipment replacement time data;

[0053] Furthermore, the method for processing the line life related data and the pre-processed line load data includes:

[0054] The aging coefficient of the transmission line is calculated based on the line life related data and the pre-processed line load data. The specific calculation formula of the aging coefficient is:

[0055] ;

[0056] Get the aging coefficient ,in, is the line current data, is the line temperature data, is the ambient humidity data, 、 and is the weight factor and satisfies ;

[0057] Divide the aging threshold by the mean of the aging coefficient to obtain the equipment replacement time data;

[0058] Adaptive scheduling module for dynamically allocating transmission line loads based on equipment replacement time data;

[0059] Furthermore, the method of dynamically allocating the load of the transmission line based on the equipment replacement time data includes:

[0060] E1, when the device replacement time data is less than or equal to the replacement threshold, the device replacement time data is transmitted to step E2; when the device replacement time data is greater than the replacement threshold, it is not processed;

[0061] E2 calculates the current command based on the equipment replacement time data. The specific calculation formula for the current command is:

[0062] ;

[0063] Get current instruction ,in, To change the threshold, is the attenuation coefficient;

[0064] E3 transmits current instructions to smart circuit breakers through power system standard protocols and adjusts transmission line loads based on the smart circuit breakers;

[0065] A visual reminder module is used to analyze the fault decision report, transmit the fault decision report based on the analysis results, and store the overall data set;

[0066] Furthermore, the methods for analyzing the fault decision report include:

[0067] When the fault decision report is a normal report, only the system log is generated;

[0068] When the fault decision report is a fluctuation report, the fault decision report will be sent to the operation and maintenance engineer's mailbox receiving terminal, and a reminder message will be sent through a pop-up window on the visualization panel;

[0069] When the fault decision report is a fault report, the fault decision report is sent to the mailbox receiving end of the operation and maintenance engineer and the operation and maintenance supervisor, and a reminder text message is sent to the mobile phone receiving end of the operation and maintenance engineer and the operation and maintenance supervisor via SMS;

[0070] The overall data set includes line fault related data set, line life related data set, fault prediction value, fault threshold interval, fault decision report and current instruction;

[0071] Further, S1: collecting line fault related data sets, the line fault related data sets including line length data, line resistance data and line load data;

[0072] S2: Collecting data sets related to line life, including line current data, line temperature data, and ambient humidity data;

[0073] S3: Preprocess line fault related data to obtain a fault feature data set;

[0074] S4: Analyze the fault feature data set to obtain the fault prediction value;

[0075] S5: Analyze the fault prediction value, judge the analysis result, and obtain a fault decision report;

[0076] S6: Optimize the fault threshold interval based on the fault prediction value to obtain a new fault threshold interval;

[0077] S7: Processing the line life related data and the pre-processed line load data to obtain equipment replacement time data;

[0078] S8: Dynamically distribute transmission line load based on equipment replacement time data;

[0079] S9: Analyze the fault decision report, transmit the fault decision report based on the analysis result, and store the overall data set.

[0080] The technical effects and advantages of the power transmission line operation status monitoring system based on power transmission management of the present invention are as follows:

[0081] The present invention collects line fault related data sets, which include line length data, line resistance data, and line load data; collects line life related data sets, which include line current data, line temperature data, and ambient humidity data; pre-processes the line fault related data to obtain a fault feature data set; analyzes the fault feature data set to obtain a fault prediction value; analyzes the fault prediction value, judges the analysis result, obtains a fault decision report; optimizes the fault threshold interval based on the fault prediction value to obtain a new fault threshold interval; processes the line life related data and the pre-processed line load data to obtain equipment replacement time data; dynamically allocates the transmission line load based on the equipment replacement time data; analyzes the fault decision report; and transmits the fault decision report based on the analysis result. And the overall data set is stored, so that the system has the ability to predict transmission line faults while being able to accurately distinguish between transmission line faults and normal fluctuations of the transmission line, thereby greatly reducing the false alarms of the system caused by fluctuations in line operation, and further causing waste of maintenance resources. In addition, the present invention also accurately monitors the actual service life of the transmission line by establishing an equipment aging early warning module, greatly reducing the shortening of the service life of the transmission line caused by the complex use environment, and then leading to transmission line operation failures, which in turn affect people's normal production and life. At the same time, by taking corresponding load reduction measures for transmission lines with poor service life, the safety of transmission lines with poor service life is greatly improved. Overall, the present invention has the significant advantages of strong ability to distinguish between faults and fluctuations, large replacement cycle detection effect and high dynamic load optimization safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 Schematic diagram of a power transmission line operation status monitoring system based on power transmission management of the present invention;

[0083] Figure 2 Schematic diagram of the method for monitoring the operating status of a transmission line based on power transmission management of the present invention. DETAILED DESCRIPTION

[0084] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0085] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a," "an," "the," and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.

[0086] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0087] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0088] In practice, the server-side device deployed in the power transmission management-based transmission line operation status monitoring system may consist of one or more devices. The aforementioned power transmission management-based transmission line operation status monitoring system can be implemented as a service instance, a virtual machine, or hardware devices. For example, the power transmission management-based transmission line operation status monitoring system can be implemented as a service instance deployed on one or more devices in a cloud node. Simply put, the power transmission management-based transmission line operation status monitoring system can be understood as software deployed on a cloud node, which provides the power transmission management-based transmission line operation status monitoring system to each user terminal. Alternatively, the power transmission management-based transmission line operation status monitoring system can be implemented as a virtual machine deployed on one or more devices in a cloud node. Application software for managing each user terminal is installed in the virtual machine. Alternatively, the power transmission management-based transmission line operation status monitoring system can be implemented as a server-side device composed of multiple hardware devices of the same or different types, with one or more hardware devices configured to provide the power transmission management-based transmission line operation status monitoring system to each user terminal.

[0089] In terms of implementation, the power transmission management-based transmission line operation status monitoring system and the user end are mutually compatible. Specifically, if the power transmission management-based transmission line operation status monitoring system is an application installed on a cloud service platform, the user end is the client that establishes a communication connection with the application; alternatively, if the power transmission management-based transmission line operation status monitoring system is implemented as a website, the user end is implemented as a webpage; or alternatively, if the power transmission management-based transmission line operation status monitoring system is implemented as a cloud service platform, the user end is implemented as a mini-program within an instant messaging application.

[0090] like Figure 1 2 is a system architecture diagram of a power transmission line operation status monitoring system based on power transmission management provided by an embodiment of the present invention.

[0091] The power transmission line operation status monitoring system based on power transmission management of the present invention can be set in a cloud server. In terms of implementation, it can be used as one or more service devices, or it can be installed as an application on the cloud (such as a mobile service operator's server, server cluster, etc.), or it can be developed as a website. According to the functions implemented, the power transmission line operation status monitoring system based on power transmission management can include a line fault related data acquisition module, a line life related data acquisition module, a fault related data preprocessing module, a fault prediction value generation module, a fault judgment decision module, a fault false alarm optimization module, an equipment aging warning module, an adaptive scheduling module and a visual reminder module. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0092] In an embodiment of the present invention, in the transmission line operation status monitoring system based on power transmission management, each of the above modules can be implemented independently and called with other modules. The call here can be understood as a module that can connect to multiple modules of another type and provide corresponding services to the multiple modules connected to it. For example, the sharing evaluation module can call the same information acquisition module to obtain the information collected by the information acquisition module. Based on the above characteristics, in the transmission line operation status monitoring system based on power transmission management provided by an embodiment of the present invention, the application scope of the transmission line operation status monitoring system architecture based on power transmission management can be adjusted by adding modules and directly calling them without modifying the program code, thereby realizing cluster-type horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the transmission line operation status monitoring system based on power transmission management. In actual applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0093] Example 1

[0094] See also Figure 1 As shown, the power transmission line operation status monitoring system based on power transmission management described in this embodiment includes:

[0095] The line fault related data acquisition module is used to acquire line fault related data sets, which include line length data, line resistance data and line load data;

[0096] It should be explained that the length of the transmission line in the specified area is collected by a laser rangefinder to obtain the line length data; the sample resistance value of the transmission line in the specified area is collected by a wire resistance tester and multiplied by the line length data to obtain the line resistance data; the maximum load value of the transmission line in the specified area is collected by the equipment nameplate to obtain the line load data;

[0097] The line life related data acquisition module is used to acquire line life related data sets, which include line current data, line temperature data and ambient humidity data;

[0098] It should be explained that the Rogowski coil current sensor is used to collect the real-time current load of the transmission line in the specified area to obtain the line current data; the contact temperature sensor is used to collect the real-time temperature data of the transmission line in the specified area to obtain the line temperature data; the capacitive humidity sensor is used to collect the ambient humidity value in the specified area to obtain the ambient humidity data;

[0099] The fault-related data preprocessing module is used to preprocess the line fault-related data to obtain a fault feature data set;

[0100] Furthermore, the method of pre-processing the line fault related data includes:

[0101] Q1, using linear interpolation and minimum-maximum normalization calculation formula, clean and normalize the line fault related data;

[0102] Q2, through line length data Multiply by the line resistance data , get the conductive characteristic data ;

[0103] Q3, extract redundant features from line load data. The specific calculation formula for redundant feature extraction is:

[0104] ;

[0105] Get redundant feature data ,in, is the line load data, is the standard load data;

[0106] Q4, pack the conductive feature data and redundant feature data to obtain the fault feature data set;

[0107] The fault prediction value generating module is used to analyze the fault feature data set to obtain a fault prediction value;

[0108] Furthermore, the steps of analyzing the fault feature data set include:

[0109] Step 1: Obtain a set of historical fault feature data sets stored in the database, compare them with the current time based on the timestamp, and mark them in corresponding groups from small to large. The marking results are W1, W2, W3, ..., Wn, and the marking results are used as the sample set;

[0110] Step 2: Divide the sample set into a 70% training set, a 15% test set, and a 15% validation set, and build a fault prediction model based on the sample set;

[0111] Step 3: Based on the training set, substitute the calculation formula:

[0112] ;

[0113] Get the first fault prediction value ,in, is the activation function, is the time window size of the historical fault feature dataset, For the The time decay weight factor of the historical fault feature dataset, is a composite kernel function, For the Group historical fault feature datasets, is the fault feature dataset, is the strength coefficient, For the Group and The cross-weight factor of the historical conductive characteristic data and historical redundant characteristic data of the group, For the The historical conductive characteristic data of the group, For the Historical redundant feature data of the group;

[0114] It should be explained that the activation function is used to constrain the output values ​​in the brackets to be within the range of (0, 1); the composite kernel function is used to identify the current state that is highly similar to the historical fault state;

[0115] Step 4: Based on the training set, substitute the calculation formula:

[0116] ;

[0117] Get the second fault prediction value ,in, is the Euclidean distance symbol;

[0118] Step 5: Based on the training set, substitute the calculation formula:

[0119] ;

[0120] Get the third fault prediction value ,in, is the mean of the historical fault feature dataset, is the transpose operation of the matrix, is the inverse matrix of the covariance matrix;

[0121] Step 6: Calculate the fault prediction value based on the first fault prediction value, the second fault prediction value, and the third fault prediction value. The specific calculation formula is:

[0122] ;

[0123] Get the fault prediction value ,in, is the hyperbolic tangent function, is the Sigmoid activation function;

[0124] It should be explained that the hyperbolic tangent function is used to scale the output values ​​in the brackets to the range of [-1, 1]; the Sigmoid activation function is used to map the output values ​​in the brackets to the range of [0, 1].

[0125] Step 7: Output the fault prediction value to the fault judgment decision module;

[0126] The fault judgment and decision module is used to analyze the fault prediction value, judge the analysis result, and obtain a fault decision report;

[0127] Furthermore, the fault prediction value is analyzed and the method for judging the analysis result includes:

[0128] W1, based on the discrimination threshold group, discriminates the fault prediction value. The specific calculation formula group for discrimination is:

[0129] ;

[0130] Get analysis results ,in, is the rate of change of the fault prediction value, is the time rate of change, is the rate of change of line length data, and are the first discrimination threshold and the second discrimination threshold respectively, For other situations;

[0131] It should be explained that the discrimination threshold group includes a first discrimination threshold and a second discrimination threshold;

[0132] When the analysis result is 1, it indicates that the fault prediction value is a real fault, and the fault prediction value is output to step W2. When the analysis result is 0, it indicates that the fault prediction value is a normal fluctuation and is not processed.

[0133] W2, classifies the analysis results based on the fault threshold interval (R1, R2);

[0134] When the fault prediction value is less than or equal to R1, a normal report is generated; when the fault prediction value is greater than R2 and less than or equal to R2, a fluctuation report is generated; when the fault prediction value is greater than R2, a fault report is generated;

[0135] W3, a normal report includes a statement that the current transmission line is predicted to have a low probability of failure and that staff are requested to carry out inspections according to the scheduled inspection cycle;

[0136] The fluctuation report includes an explanation of the predicted fault possibility of the current transmission line, and asks the staff to adjust the reactive power compensation and detection parameter changes;

[0137] The fault report includes a statement indicating that the current transmission line is highly likely to fail, and requests staff to isolate the transmission lines in the designated area and dispatch a nearby repair team to conduct on-site inspections and repairs.

[0138] W4, packages the normal report, fluctuation report and fault report to obtain the fault decision report;

[0139] The fault false alarm optimization module is used to optimize the fault threshold interval based on the fault prediction value to obtain a new fault threshold interval;

[0140] Furthermore, methods for optimizing the fault threshold interval based on the fault prediction value include:

[0141] By substituting into the calculation formula group:

[0142] ;

[0143] Get the new fault threshold interval ( , ),in, is the mean of the fault prediction values, is the standard deviation of the fault prediction value;

[0144] Return the new fault threshold interval and substitute it into step W2 in the fault judgment decision module;

[0145] The equipment aging warning module is used to process line life related data and pre-processed line load data to obtain equipment replacement time data;

[0146] Furthermore, the method for processing the line life related data and the pre-processed line load data includes:

[0147] The aging coefficient of the transmission line is calculated based on the line life related data and the pre-processed line load data. The specific calculation formula of the aging coefficient is:

[0148] ;

[0149] Get the aging coefficient ,in, is the line current data, is the line temperature data, is the ambient humidity data, 、 and is the weight factor and satisfies ;

[0150] Divide the aging threshold by the mean of the aging coefficient to obtain the equipment replacement time data;

[0151] It should be explained that the aging threshold is the service life of the standard transmission line; the mean aging coefficient is the mean of the aging coefficients calculated at a fixed time within a preset time period. For example, within 24 hours, the aging coefficient is calculated once per hour, and all aging coefficients within 24 hours are added together and divided by the number of calculations to obtain the mean aging coefficient.

[0152] The adaptive scheduling module is used to dynamically allocate the load of the transmission line based on the equipment replacement time data;

[0153] Furthermore, the method of dynamically allocating the load of the transmission line based on the equipment replacement time data includes:

[0154] E1, when the device replacement time data is less than or equal to the replacement threshold, the device replacement time data is transmitted to step E2; when the device replacement time data is greater than the replacement threshold, it is not processed;

[0155] It should be explained that the replacement threshold is the minimum permissible service life of the preset transmission line;

[0156] E2 calculates the current command based on the equipment replacement time data. The specific calculation formula for the current command is:

[0157] ;

[0158] Get current instruction ,in, To change the threshold, is the attenuation coefficient;

[0159] E3 transmits current instructions to smart circuit breakers through power system standard protocols and adjusts transmission line loads based on the smart circuit breakers;

[0160] The visual reminder module is used to analyze the fault decision report, transmit the fault decision report according to the analysis result, and store the overall data set;

[0161] Further, the methods for analyzing the fault decision report include:

[0162] When the fault decision report is a normal report, only the system log is generated;

[0163] When the fault decision report is a fluctuation report, the fault decision report will be sent to the operation and maintenance engineer's mailbox receiving terminal, and a reminder message will be sent through a pop-up window on the visualization panel;

[0164] When the fault decision report is a fault report, the fault decision report is sent to the mailbox receiving end of the operation and maintenance engineer and the operation and maintenance supervisor, and a reminder text message is sent to the mobile phone receiving end of the operation and maintenance engineer and the operation and maintenance supervisor via SMS;

[0165] The overall data set includes line fault related data set, line life related data set, fault prediction value, fault threshold interval, fault decision report and current instruction;

[0166] The present embodiment has the following beneficial effects: a line fault related data set is collected, the line fault related data set includes line length data, line resistance data and line load data, a line life related data set is collected, the line life related data set includes line current data, line temperature data and ambient humidity data, the line fault related data is preprocessed to obtain a fault feature data set, the fault feature data set is analyzed to obtain a fault prediction value, the fault prediction value is analyzed, and the analysis result is judged to obtain a fault decision report, the fault threshold interval is optimized based on the fault prediction value to obtain a new fault threshold interval, the line life related data and the preprocessed line load data are processed to obtain equipment replacement time data, the transmission line load is dynamically allocated based on the equipment replacement time data, the fault decision report is analyzed, and the fault decision report is judged based on the analysis result. The system can predict transmission line faults and store the overall data set, so that the system can accurately distinguish between transmission line faults and normal fluctuations of the transmission line while predicting transmission line faults, thereby greatly reducing the false alarms of the system caused by fluctuations in line operation, and further causing waste of maintenance resources. In addition, the present invention also accurately monitors the actual service life of the transmission line by establishing an equipment aging early warning module, greatly reducing the shortening of the service life of the transmission line caused by the complex use environment, and then causing transmission line operation failures, which in turn affect people's normal production and life. At the same time, by taking corresponding load reduction measures for transmission lines with poor service life, the safety of transmission lines with poor service life is greatly improved. In general, the present invention has the significant advantages of strong ability to distinguish between faults and fluctuations, large replacement cycle detection effect and high degree of safety in dynamic load optimization.

[0167] Example 2

[0168] See also Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description of Example 1. A method for monitoring the operating status of a transmission line based on power transmission management is provided, the method comprising: S1: collecting a line fault related data set, the line fault related data set comprising line length data, line resistance data, and line load data;

[0169] S2: Collecting data sets related to line life, including line current data, line temperature data, and ambient humidity data;

[0170] S3: Preprocess line fault related data to obtain a fault feature data set;

[0171] S4: Analyze the fault feature data set to obtain the fault prediction value;

[0172] S5: Analyze the fault prediction value, judge the analysis result, and obtain a fault decision report;

[0173] S6: Optimize the fault threshold interval based on the fault prediction value to obtain a new fault threshold interval;

[0174] S7: Processing the line life related data and the pre-processed line load data to obtain equipment replacement time data;

[0175] S8: Dynamically distribute transmission line load based on equipment replacement time data;

[0176] S9: Analyze the fault decision report, transmit the fault decision report based on the analysis result, and store the overall data set.

[0177] Example 3

[0178] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0179] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0180] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0181] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A power transmission line operation status monitoring system based on power transmission management, characterized in that: The system includes: a fault-related data preprocessing module, a fault prediction value generation module, a fault judgment and decision module, a fault false alarm optimization module, an equipment aging early warning module, and an adaptive scheduling module, wherein: The fault-related data preprocessing module is used to preprocess the line fault-related data to obtain a fault feature data set; The fault prediction value generating module is used to analyze the fault feature data set to obtain a fault prediction value; The fault judgment and decision module is used to analyze the fault prediction value, judge the analysis result, and obtain a fault decision report; The fault false alarm optimization module is used to optimize the fault threshold interval based on the fault prediction value to obtain a new fault threshold interval; The equipment aging warning module is used to process line life related data and pre-processed line load data to obtain equipment replacement time data; The adaptive scheduling module is used to dynamically allocate the load of the transmission line based on the equipment replacement time data.

2. The power transmission line operation status monitoring system based on power transmission management according to claim 1 is characterized in that: It also includes line fault related data collection module, line life related data collection module and visual reminder module; The line fault related data acquisition module is used to acquire line fault related data sets, which include line length data, line resistance data and line load data; The line life related data acquisition module is used to acquire line life related data sets, which include line current data, line temperature data and ambient humidity data; The visual reminder module is used to analyze the fault decision report, transmit the fault decision report according to the analysis result, and store the overall data set.

3. The power transmission line operation status monitoring system based on power transmission management according to claim 1, characterized in that: Methods for preprocessing line fault related data include: Q1, using linear interpolation and minimum-maximum normalization calculation formula, clean and normalize the line fault related data; Q2, through line length data Multiply the line resistance data , get the conductive characteristic data ; Q3, extract redundant features from line load data. The specific calculation formula for redundant feature extraction is: ； Get redundant feature data ,in, is the line load data, is the standard load data; Q4: Pack the conductive feature data and redundant feature data to obtain a fault feature data set.

4. The power transmission line operation status monitoring system based on power transmission management according to claim 1, characterized in that: The steps for analyzing the fault signature dataset include: Step 1: Obtain a set of historical fault feature data sets stored in the database, compare them with the current time based on the timestamp, and mark them in corresponding groups from small to large. The marking results are W1, W2, W3, ..., Wn, and the marking results are used as the sample set; Step 2: Divide the sample set into a 70% training set, a 15% test set, and a 15% validation set, and build a fault prediction model based on the sample set; Step 3: Based on the training set, substitute the calculation formula: ； Get the first fault prediction value ,in, is the activation function, is the time window size of the historical fault feature dataset, For the The time decay weight factor of the historical fault feature dataset, is a composite kernel function, For the Group historical fault feature datasets, is the fault feature dataset, is the strength coefficient, For the Group and The cross-weight factor of the historical conductive characteristic data and historical redundant characteristic data of the group, For the The historical conductive characteristic data of the group, For the Historical redundant feature data of the group; Step 4: Based on the training set, substitute the calculation formula: ； Get the second fault prediction value ,in, is the Euclidean distance symbol; Step 5: Based on the training set, substitute the calculation formula: ； Get the third fault prediction value ,in, is the mean of the historical fault feature dataset, is the transpose operation of the matrix, is the inverse matrix of the covariance matrix; Step 6: Calculate the fault prediction value based on the first fault prediction value, the second fault prediction value, and the third fault prediction value. The specific calculation formula is: ； Get the fault prediction value ,in, is the hyperbolic tangent function, is the Sigmoid activation function; Step 7: Output the fault prediction value to the fault judgment decision module.

5. The power transmission line operation status monitoring system based on power transmission management according to claim 1, characterized in that: Methods for analyzing the fault prediction value and judging the analysis results include: W1, based on the discrimination threshold group, discriminates the fault prediction value. The specific calculation formula group for discrimination is: ； Get analysis results ,in, is the rate of change of the fault prediction value, is the time rate of change, is the rate of change of line length data, and are the first discrimination threshold and the second discrimination threshold respectively, For other situations; When the analysis result is 1, it indicates that the fault prediction value is a real fault, and the fault prediction value is output to step W2. When the analysis result is 0, it indicates that the fault prediction value is a normal fluctuation and is not processed. W2, classifies the analysis results based on the fault threshold interval (R1, R2); When the fault prediction value is less than or equal to R1, a normal report is generated; when the fault prediction value is greater than R2 and less than or equal to R2, a fluctuation report is generated; when the fault prediction value is greater than R2, a fault report is generated; W3, a normal report includes a statement that the current transmission line is predicted to have a low probability of failure and that staff are requested to carry out inspections according to the scheduled inspection cycle; The fluctuation report includes an explanation of the predicted fault possibility of the current transmission line, and asks the staff to adjust the reactive power compensation and detection parameter changes; The fault report includes a statement indicating that the current transmission line is highly likely to fail, and requests staff to isolate the transmission lines in the designated area and dispatch a nearby repair team to conduct on-site inspections and repairs. W4, packages the normal report, fluctuation report and fault report to obtain the fault decision report.

6. The power transmission line operation status monitoring system based on power transmission management according to claim 1, characterized in that: Methods for optimizing the fault threshold interval based on the fault prediction value include: By substituting into the calculation formula group: ； Get the new fault threshold interval ( , ),in, is the mean of the fault prediction values, is the standard deviation of the fault prediction value; The new fault threshold interval is returned and substituted into step W2 in the fault judgment decision module.

7. The power transmission line operation status monitoring system based on power transmission management according to claim 1, characterized in that: Methods for processing line life related data and pre-processed line load data include: The aging coefficient of the transmission line is calculated based on the line life related data and the pre-processed line load data. The specific calculation formula of the aging coefficient is: ； Get the aging coefficient ,in, is the line current data, is the line temperature data, is the ambient humidity data, 、 and is the weight factor and satisfies ; Divide the aging threshold by the mean of the aging coefficient to obtain the equipment replacement time data.

8. The power transmission line operation status monitoring system based on power transmission management according to claim 1, characterized in that: Methods for dynamically allocating transmission line loads based on equipment replacement time data include: E1, when the device replacement time data is less than or equal to the replacement threshold, the device replacement time data is transmitted to step E2; when the device replacement time data is greater than the replacement threshold, it is not processed; E2 calculates the current command based on the equipment replacement time data. The specific calculation formula for the current command is: ； Get current instruction ,in, To change the threshold, is the attenuation coefficient; E3 transmits current instructions to smart circuit breakers through the power system standard protocol and adjusts the transmission line load based on the smart circuit breaker.

9. The power transmission line operation status monitoring system based on power transmission management according to claim 1, characterized in that: Methods for analyzing the failure decision report include: When the fault decision report is a normal report, only the system log is generated; When the fault decision report is a fluctuation report, the fault decision report will be sent to the operation and maintenance engineer's mailbox receiving terminal, and a reminder message will be sent through a pop-up window on the visualization panel; When the fault decision report is a fault report, the fault decision report is sent to the mailbox receiving end of the operation and maintenance engineer and the operation and maintenance supervisor, and a reminder text message is sent to the mobile phone receiving end of the operation and maintenance engineer and the operation and maintenance supervisor via SMS; The overall data set includes line fault related data set, line life related data set, fault prediction value, fault threshold interval, fault decision report and current instruction.

10. A method for monitoring the operating status of a power transmission line based on power transmission management, implemented by a system for monitoring the operating status of a power transmission line based on power transmission management according to any one of claims 1 to 9, characterized in that: The following steps are included: S1: Collect line fault related data sets, which include line length data, line resistance data, and line load data; S2: Collecting data sets related to line life, including line current data, line temperature data, and ambient humidity data; S3: Preprocess line fault related data to obtain a fault feature data set; S4: Analyze the fault feature data set to obtain the fault prediction value; S5: Analyze the fault prediction value, judge the analysis result, and obtain a fault decision report; S6: Optimize the fault threshold interval based on the fault prediction value to obtain a new fault threshold interval; S7: Processing the line life related data and the pre-processed line load data to obtain equipment replacement time data; S8: Dynamically distribute transmission line load based on equipment replacement time data; S9: Analyze the fault decision report, transmit the fault decision report based on the analysis result, and store the overall data set.

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